// construct PMF with the given background and delta, from a PMF representing the observed profile.
// if pmf unknown (empty) return the background
// NB The input PMF need NOT be normalized. The returned PMF is normalized.
//
static PMF<Allele>
makePMF(const PMF<Allele> &pmf,
const PMF<Allele> &background,
float delta,
bool sparse)
{
// How much background to add in?
// If the input PMF is not normalized then we make up the difference
// with background. Otherwise we add in delta.
double bglevel = std::max((double)delta, 1 - pmf.sum());
PMF<Allele> ret;
if (!sparse)
{
// This implementation ensures there is an element of the
// returned PMF for each element of the background
ret = background;
if (! pmf.empty())
{
ret *= bglevel;
ret += pmf;
ret.normalize();
}
Assert2(ret.size() == background.size(), "PMF contains unknown alleles");
}
else
{
// This implementation allows the PMF (and therefore the HMatrix) to remain sparse
if (pmf.empty())
{
ret = background;
}
else if (bglevel == 0)
{
ret = pmf;
ret.normalize();
}
else
{
ret = background;
ret *= bglevel;
ret += pmf;
ret.normalize();
}
}
// cout << "pmf = " << pmf << endl;
// cout << "background = " << background << endl;
// cout << "bglevel = " << bglevel << endl;
// cout << "ret = " << ret << endl << endl;
return ret;
}
